There has been a surge of interest in network representation learning for static network data in recent years. Generally speaking, NRL methods fall into one of two categories: generative or descriptive. Descriptive methods like LINE [1]}, DeepWalk [2]}, and node2vec [3]} are designed to identify features by scanning local structures of the network like neighborhoods and shortest paths. Descriptive NRL methods have only recently been extended to the setting of network samples and have largely relied on adaptations of static methods [4]}. Descriptive methods have caught a lot of attention in the last few years for at least two major reasons: (i) they are fast – algorithms are designed to quickly embed large graphs (millions of nodes) in minutes, (ii) the features identified are highly predictive of other node-based attributes and motifs like community structure. In many situations, descriptive methods rely on the application of a neural network architecture and the identified features have been shown to be approximations of other well-understood unsupervised strategies like non-negative matrix factorization [5]}. Generative methods, on the other hand, characterize lower-dimensional features of the network through a generative probabilistic model. The latent space model from [6]}, for example, is a common model-based embedding technique that embeds the observed network onto Euclidean space based on node positions after accounting for other network features and attributes in the model. Unlike typical descriptive methods, generative models like the latent space model benefit from interpretability of node embeddings but are comparatively computationally slow. PCAN and sPCAN are fast descriptive methods that enjoy interpretability only directly available to generative methods.
